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Fig. 6. Separator to detect grass with the help of the laser scanner [5].
Fig. 7. Detection of the grass though the laser scanner [5].
We start our discussion of related research with the robot
presented in [11]. The robot could take a long tour through
Munich without a prior created map or GPS information.
Instead, the robot was using its sensors to react locally in
a safe manner and asked humans for information about the
direction. This was done by approaching humans and the
recognition of basic commands to derive the direction of the
desired destination. In contrast, our robot has a prior created
map which allows it to move autonomously without asking
for directions. This is also desirable in the case of a transport
robot which should transport goods to a customer.
In [12] the method to deal with large maps was described.
The authors use a topological map to allow an efficient
representation of large areas. The vertices in the topological
map are spots of interests such as a square or a crossing.
The edges represent paths between these places. For each
edge, a traversal behavior is defined. Thus, one can use
different behaviors to perform the traversal. With the help of
this method, the robot could drive autonomously in a park.
Our robot uses, in contrast, a topological map which contains
enough information to allow the robot to be always localized
not only in interesting places. Furthermore, the robot uses a
denser road map allowing it to plan its route more accurate.
A very close related work to ours was presented in [13].
The robot navigated more than 3km in the city Freiburg in
an autonomous fashion. To localize itself, the robot used a
topological map where each vertex in the graph contains
a map of one part of the environment. In contrast, our
approach additionally used the GPS signal for estimating
the robot pose within the particle filter. To navigate the robot, the method presented in [13] created a high-level
plan using the graph of the topological map. Each vertex is
connected to those vertices in the graph which allow moving
between these two locations. Thus, using this graph the
robot can derive a simple high-level plan for the navigation.
Whereas the robot uses a planner on grid map basis to
navigate between different vertices of the topological map.
This contrasts with our approach as we use a finer grained
road map for the high-level planning which allows us to
choose the path more precisely.
VI. CONCLUSION AND FUTURE WORK
The transportation of goods is an essential part of our
today’s economy. The transportation often takes place in
outdoor environments by delivering goods to costumers. To
provide cost-efficient and flexible deliveries, robots are a
promising solution.
In this paper, we presented an autonomous transport robot
which is capable of navigating in large scale outdoor envi-
ronments. To perform this transportation, the robot addresses
the problem of a large-scale environment, uneven ground,
and grass which should only be traversed if necessary. To
deal with the large scale of the environment the robot uses a
topological map. This map stores areas of the environment
which are loaded on demand. This allows that the robot
only needs to keep a small part of the environment in its
memory and perform the localization on it. We furthermore
showed how the robot can exploit the topological map to
switch between the different parts to allow the robot to be
localized during the complete delivery. To deal with the
uneven ground, the robot builds an elevation map for its
local environment. Afterward, the robot determines within
the elevation map dangerous terrain and avoids it. To deal
with the grass we have shown a simple solution with a linear
classification for laser scan measurements. This detection
allows the robot to detect grass precisely enough to avoid
the grass if possible.
The robot presented in this paper mainly used several laser
scanners to localize itself and it is left for future work to add
more sensors to perform localization as well as navigation.
Especially cameras would be of interest as they allow a
detailed localization in many areas which don’t offer features
for a laser scanner. The additional use of a camera would
increase the quality of the terrain classification.
REFERENCES
[1] P. R. Wurman, R. D’Andrea, and M. Mountz, “Coordinating
hundreds of cooperative, autonomous vehicles in warehouses,”
in Proceedings of the 19th national conference on Innovative
applications of artificial intelligence - Volume 2, ser. IAAI’07.
AAAI Press, 2007, pp. 1752–1759. [Online]. Available:
http://dl.acm.org/citation.cfm?id=1620113.1620125
[2] E. Guizzo, “Three Engineers, Hundreds of Robots, One Warehouse,”
Spectrum, IEEE, vol. 45, no. 7, pp. 26–34, 2008.
[3] C. Mu¨hlbacher, S. Gspandl, M. Reip, and G. Steinbauer, “Improving
dependability of industrial transport robots using model-based tech-
niques,” in Robotics and Automation (ICRA), 2016 IEEE International
Conference on. IEEE, 2016, pp. 3133–3140.
[4] S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics. MIT press,
2005.
43
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Titel
- Proceedings of the OAGM&ARW Joint Workshop
- Untertitel
- Vision, Automation and Robotics
- Autoren
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Verlag
- Verlag der Technischen Universität Graz
- Ort
- Wien
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Schlagwörter
- Tagungsband
- Kategorien
- International
- Tagungsbände